# Incrementality Study of the Canada Small Business Financing Program, March 2018

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Aussi offert en français sous le titre Étude sur l'effet d'accroissement du Programme de financement des petites entreprises du Canada, mars 2018.

## Executive Summary

The Canada Small Business Financing Program (CSBFP) is a statutory loan loss-sharing program administered by Innovation, Science and Economic Development Canada (ISED) that helps Canadian small businesses obtain access to financing. Under the CSBFP, ISED and commercial lenders share the risk of providing small businesses with term loans for real property, equipment or leasehold improvements. The primary objective of the CSBFP is to increase financing for small businesses by extending financing that would not otherwise be available (full incrementality) or would be available under less favourable terms (partial incrementality).

This paper investigates the level of full incrementality of the CSBFP by analyzing data from the Survey on Financing and Growth of Small and Medium Enterprises, 2014. The main finding of the paper is that the CSBFP is 69 percent fully incremental. In other words, 69 percent of debt financing requests from small businesses would have been denied without the existence of the CSBFP, which is consistent with previous studies. Partial incrementality will be examined in the near future. It is expected that once partial incrementality is considered in concert with full incrementality, the program will be closer to 100% incremental.

## 1. Introduction

Small businessesFootnote 1 are well recognized for playing a fundamental role in the Canadian economy (Seens and Song, 2015; Coe, 2016; Chandler, 2012 and Riding, 2007). They represent 98 percent of the total number of employer businesses in Canada, and approximately 71 percent of total private sector employment in 2015 (Innovation, Science and Economic Development Canada, 2016c). Canadian small businesses from the private sector accounted for approximately 30 percent of the gross domestic product of the total economy (Leung et al., 2012 and Industry Canada, 2013b). This situation is not unique to Canada and may be observed in many countries such as the United States, the United Kingdom, France and Germany (Banerjee, 2014).

The Government of Canada supports small businesses through several channels. One of them is the Canada Small Business Financing Program (CSBFP). The CSBFP originated in 1961 and was launched under the Small Business Loans Act. This act has evolved over time and was renamed, in 1999, as the Canada Small Business Financing Act (CSBFA).Footnote 2 The main purpose of the CSBFP is "to increase the availability of loans for establishing, expanding, modernizing and improving small businesses." (Industry Canada, 2014). Other countries such as the United States and the United Kingdom also have a loan guarantee program for small businesses (Riding, 2001; OECD, 2013 and OECD, 2015).

Under the program, small businessesFootnote 3 may obtain, if eligibility criteria are met, financing from a lender for the following assets: real property (immovables), equipment and leasehold improvements. The loan may not exceed \$1 million, of which a maximum of \$350,000 can be used for purposes other than the purchase or improvement of real property.Footnote 4 The loan terms vary according to the type of asset: 10 years for leasehold improvements and equipment; and 15 years for real property. Also, fees other than the interest rate are applicable; they include a 2 percent registration fee and a 1.25 percent administration fee payable quarterly.

Under the CSBFP, the role of the Government of Canada is to share the risk with the lender by guaranteeing 85 percent of the lender's net eligible lossesFootnote 5 in the case of default by a borrower. As program administrator, ISED's role is to register loans, collect fees and pay lenders' eligible losses on defaulted loans (Industry Canada, 2015). During the fiscal year 2015–2016, the CSBFP registered 5,044 loans with a total value of \$879.9 million. Loans between \$125,000 and \$375,000 accounted for 74 percent of the total value of registered loans. Moreover, 40 percent of the loans' total value was for equipment. Finally, the majority of loans granted were in the accommodation and food services and retail trade sectors, which accounted for 42 percent of the total number of loans (Innovation, Science and Economic Development Canada, 2016a).

As legislated by the CSBFA of 1999, the CSBFP operates on a statutory five-year review cycle. The program is required to table a comprehensive review in both Houses of Parliament within 12 months after each review period. The purposes of the review are to examine the extent to which the program meets its objectives, outline the rationale and relevance of the program in meeting the financing needs of small and medium-sized enterprises (SME)s, and suggest possible program improvements.

An effective way to assess if the program has achieved its goal to increase loan availability to small businesses is to determine whether financing would have been available without the program's existence. This program objective is called incrementality or additionality (Riding, 2001). While more details about how incrementality is measured are provided in Sections 3 and 5, it is important to note that this study only measures full incrementality. Partial incrementality—loans that would have been made but under less favourable lending conditions such as higher interest rates or lower loan amounts—will be estimated in the upcoming 2018 Lender Awareness and Satisfaction Study. When full and partial incrementality are combined, almost all CSBFP loans can be considered incremental.

This paper aims to estimate the incrementality of the CSBFP using recent data, i.e., from the Survey on Financing and Growth of Small and Medium Enterprises, 2014 and other data sources. Another objective is to update the results obtained by Seens and Song (2015). The paper is divided as follows:

• Section 3 presents a literature review on incrementality in Canada over time;
• Section 4 contains a description of the dataset used in this study;
• Section 5 explains the methodology and the econometric model;
• Section 6 describes variables;
• Section 7 discusses the results of the regression; and
• Section 8 concludes the paper by summarizing the main finding.

The Survey on Financing and Growth of Small and Medium Enterprises, 2014 reveals interesting facts on that topic. Only 51 percent of SMEs requested external financing in 2014. External financing takes different forms, such as debt financing (non-residential mortgages, lines of credit, term loans, credit cards), lease financing, trade credit financing, equity financing and government financing (grants, subsidies or non-repayable contributions). For those that did not request external financing, the most popular reason given was that they did not require financing (Table 1).

Table 1: Reason for Not Seeking External Financing by Business Size (percentage)
Reasons 1–4
employees
5–19
employees
20–99
employees
100–499
employees
Financing not required 87.4 89.0 93.0 86.4
Thought the request would be turned down 1.9 2.2 X X
Applying for financing is too difficult or time consuming 2.5 X 2.0 X
Cost of financing is too high 1.0 X X 0.0
Unaware of financing sources available to the business 3.6 2.3 X X
Other 3.6 4.4 2.5 8.3

Note: "X" indicates that data were suppressed to meet confidentiality requirements of the Statistics Act.
Source: Statistics Canada, Survey on Financing and Growth of Small and Medium Enterprises, 2014.

In particular, only a few SMEs, about 28 percent, requested debt financing. Figure 1 provides a partial answer to the aforementioned question. Between 9 and 11 percent of small businesses (1 to 99 employees) answered that obtaining financing was a major obstacle to business growth in 2014, compared with 5 percent for medium businesses (100 to 499 employees). Thus, in terms of percentage, there are around twice as many small businesses than medium businesses that were concerned with obtaining financing. However, among all the obstacles to business growth pointed out by SMEs, obtaining financing was at the bottom of the list (Table 2).

#### Figure 1: Obtaining Financing as an Obstacle to Business Growth by Business Size, 2014

Description of Figure 1
Obtaining Financing as an Obstacle to Business Growth by Business Size, 2014
100–499 employees 4.5%
20–99 employees 8.8%
5–19 employees 10.6%
1–4 employees 8.5%
Table 2: Major Obstacles to Business Growth of SMEs (percentage) by Business Size
- 1–4
employees
5–19
employees
20–99
employees
100–499
employees
Fluctuations in consumer demand 18.4 20.4 21.0 19.4
Increasing competition 17.6 19.6 20.9 22.8
Recruiting and retaining skilled employees 14.7 18.9 18.9 16.4
Rising cost of inputs 14.6 18.9 19.3 16.9
Government regulations 14.2 16.9 16.4 14.0
Maintaining sufficient cash-flow or managing debt 12.7 15.4 13.0 11.6
Shortage of labour 11.5 16.9 16.7 18.5
Other 11.5 11.6 13.4 9.9
Corporate tax rate 11.1 12.6 10.9 7.9
Obtaining financing 8.5 10.6 8.8 4.5

Note: Totals may exceed 100% since businesses could declare more than one major obstacle to business growth.
Source: Statistics Canada, Survey on Financing and Growth of Small and Medium Enterprises, 2014.

The survey also provides information on the amount of debt financing requested versus authorized. Thus, it is possible to target the type of SMEs that have experienced difficulty in obtaining debt financing, i.e., those that have undergone credit rationing by lenders. Moreover, the authorized-to-requested ratio is a particularly robust financial indicator that completes the answer given by entrepreneurs in relation to business growth obstacles, as it is an objective measure rather than an opinion. The lower the authorized-to-requested ratio is, the more difficult it is to access financing. As a consequence, SMEs face credit rationing in this case. Information on the debt requested rate could also be used as an indication of the extent of debt financing requested by category of SME (start-ups, industry sector, etc.). Figure 2 shows the comparison between the debt requested rate and the authorized-to-requested ratio by business size, industry sector, business location, export orientation, business age, innovation activity, age and gender owner(s). Figure 2 is similar to Figure 21 in Industry Canada's Financing Statistics (2013a), which used data from the Survey on Financing and Growth of Small and Medium Enterprises, 2011.

We observed that start-ups (i.e. businesses that are two years old and less) struggled to obtain the financing that they asked for, as they only received 75 percent of the total amount requested. In comparison, firms aged 20 years and more received 89 percent of the total amount requested. In addition, the debt requested rate for start-ups is greater than that for established businesses, which shows that the demand for debt financing from young firms is quite important. It should be noted that start-up firms represent a significant proportion of CSBFP borrowers. In 2015–2016, just over 60 percent of CSBFP loans (in value) were granted to firms less than one year old.

Businesses with 1 to 4 employees also had more difficulties than larger ones. The authorized to requested ratio is 83 percent for micro-enterprises (1–4 employees) and 98 percent for larger businesses (100–499 employees).

Figure 2 reveals that some industry sectors had more difficulties than others. This is the case for accommodation and food services. This sector had the lowest authorized-to-requested ratio, with 67 percent. This situation reflects the fact that a large proportion of CSBFP borrowers, about 31 percent, were active in the accommodation and food services sector in 2015–2016. As mentioned by Coe (2016), this sector is generally associated with a high degree of risk for those starting an enterprise. Primary industries (agriculture, forestry, fishing and hunting, mining, quarrying, and oil and gas extraction) had the highest authorized-to-requested ratio, with about 94 percent. It is worth noting that, in general, those industries are known to have more collateral (Industry Canada, 2013a). Overall, the previous findings are similar to those from the Survey on Financing and Growth of Small and Medium Enterprises, 2011.

The previous results may show that this situation is asymptomatic of market imperfections and that SMEs suffer from a structural financing problem (Wagenvoort, 2003). Asymetric information is often cited as the major cause of distortions in the market. For example, compared with larger and well-established firms, start-ups have a shorter or non-existent credit history, they have no reputation, and information on them could be difficult to find for lenders.

#### Figure 2: Authorized-to-Requested Ratio and Debt Requested Rate by Category of SME, 2014

Description of Figure 2
Authorized-to-Requested Ratio and Debt Requested Rate by Category of SME, 2014
Enterprises Debt Requested Rate (%) Authorized-to-Requested Ratio (%)
1–4 employees 22.6 82.8
5–19 employees 31.8 81.2
20–99 employees 41.0 87.8
100–499 employees 44.9 98.2
Primary Industries 41.1 93.9
Construction 34.4 89.1
Manufacturing 30.4 87.1
Transportation and Warehousing 33.3 89.9
Professional, Scientific and Technical Services 22.4 89.1
Accommodation and Food Services 26.3 67.2
Other Services 26.9 81.2
All Other 21.3 82.0
KBI 25.9 75.5
Rural 33.6 87.5
Urban 26.7 85.7
Exporter 34.7 89.8
Non-exporter 27.2 85.0
Start-ups 36.9 75.0
3–10 years 30.8 81.4
11–20 years 27.1 88.3
More than 20 years 24.8 89.0
Innovator 35.3 87.3
Non-Innovator 23.2 84.5
Young Entrepreneurs 42.1 88.4
Majority Male-Owned 29.0 86.7
Majority Female-Owned 23.2 81.5
Average 28.1 86.1

Another issue is that SMEs could have less collateral to provide as securities. As a consequence, they are judged as riskier borrowers by lenders. Approximately 30 percent of SMEs cited insufficient collateral as the reason for being denied debt financing in 2014 and 22 percent cited poor or lack of credit experience (Figure 3). However, insufficient sales or cash-flow was the most common reason cited by businesses, with approximately 35 percent, as shown by Figure 3.

#### Figure 3: Reasons SMEs Were Denied Debt Financing, 2014

Description of Figure 3
Reasons SMEs Were Denied Debt Financing, 2014
Reason Percentage
Insufficient sales or cash-flow 35.1%
Other reason 31.2%
Insufficient collateral 30.3%
Project was considered too risky 26.9%
Poor or lack of credit experience or history 22.2%
Business operates in an unstable industry 19.5%
No reason given by credit provider 8.9%

Since SMEs are often seen as riskier borrowers, they also face higher costs, i.e., they have to borrow with higher interest rates (Figure 4). However, in this case, the interest rate difference between firm size is small and could be considered as not statistically significantly different. Moreover, the fact that the probability of delinquency is higher for smaller firms may also explain why lenders charged SMEs higher interest rates. Figure 5 shows delinquency ratesFootnote 6 by business size.Footnote 7 Clearly, it appears that the delinquency rate is higher for smaller businesses.

The previous results offer good reasons for the Government of Canada to facilitate access to financing for SMEs. The federal government, as a regulator, needs to ensure that access to debt financing is not too restricted for SMEs due to asymmetric information, market imperfections or other reasons.

#### Figure 4: Interest Rate Charged for Term Loans and Line of Credit for SMEs, 2014

Description of Figure 4
Interest Rate Charged for Term Loans and Line of Credit for SMEs, 2014
Business size Term loans Interest rate (%) Line of credit Interest Rate (%)
1–4 employees 5.3 5.6
5–19 employees 5.5 5.1
20–99 employees 4.7 4.7
100–499 employees 4.3 4.8

#### Figure 5: 90+ Day Loan Delinquency Rates, 2005 Q1–2016 Q3

Description of Figure 5
90+ Day Loan Delinquency Rates, 2005 Q1–2016 Q3
1Q05 75.8% 0.04% 0.15%
2Q05 12.2% 0.05% 0.16%
3Q05 6.6% 0.03% 0.22%
4Q05 4.9% 0.06% 0.29%
1Q06 0.5% 0.11% 0.29%
2Q06 0.05% 0.16% 0.29%
3Q06 0.65% 0.14% 0.47%
4Q06 0.65% 0.11% 0.49%
1Q07 0.66% 0.06% 0.53%
2Q07 0.67% 0.05% 0.57%
3Q07 0.69% 0.04% 0.64%
4Q07 0.74% 0.04% 0.72%
1Q08 0.83% 0.04% 0.86%
2Q08 0.95% 0.05% 1.00%
3Q08 1.06% 0.07% 1.13%
4Q08 1.20% 0.08% 1.22%
1Q09 1.39% 0.17% 1.34%
2Q09 1.51% 0.29% 1.41%
3Q09 1.49% 0.46% 1.40%
4Q09 1.29% 0.50% 1.26%
1Q10 1.05% 0.41% 1.12%
2Q10 0.84% 0.23% 0.97%
3Q10 0.70% 0.11% 0.85%
4Q10 0.62% 0.05% 0.64%
1Q11 0.60% 0.04% 0.47%
2Q11 0.62% 0.03% 0.36%
3Q11 0.63% 0.04% 0.34%
4Q11 0.61% 0.03% 0.32%
1Q12 0.59% 0.01% 0.30%
2Q12 0.57% 0.00% 0.29%
3Q12 0.57% 0.01% 0.28%
4Q12 0.54% 0.01% 0.28%
1Q13 0.48% 0.02% 0.28%
2Q13 0.41% 0.02% 0.26%
3Q13 0.38% 0.01% 0.25%
4Q13 0.38% 0.01% 0.26%
1Q14 0.41% 0.01% 0.29%
2Q14 0.41% 0.02% 0.32%
3Q14 0.42% 0.02% 0.37%
4Q14 0.46% 0.03% 0.44%
1Q15 0.54% 0.04% 0.54%
2Q15 0.60% 0.04% 0.58%
3Q15 0.60% 0.03% 0.56%
4Q15 0.57% 0.03% 0.52%
1Q16 0.52% 0.07% 0.49%
2Q16 0.46% 0.13% 0.46%
3Q16 0.47% 0.21% 0.46%

## 3. Literature Review

This section identifies the work that has been done to evaluate and measure the level of incrementality of the CSBFP over the years. The methodology adopted by researchers in Canada uses a credit scoring model. This is a two-step approach: in the first step, parameters are estimated using a logit regression applied to firms that were non-CSBFP borrowers. The model estimates a firm's probability of having its debt financing request approved or rejected. The second step consists in applying the model's estimates to CSBFP borrowers to calculate each firm's probability of being approved or rejected. The proportion of firms that the model identifies as being rejected is the measure of incrementality. Equinox (2003) and Riding et al. (2007) used this approach with data from the Survey on Financing of Small and Medium Enterprises. The authors used a sample of 382 businesses, 281 of which were non-CSBFP borrowers and 101 of which were CSBFP borrowers. Riding et al. (2007) found that the level of incrementality for the CSBFP was 75 percent. Thus, 75 percent of the debt financing requests would have been denied without the CSBFP.

Riding (2009) obtained an incrementality of approximately 80 to 85 percent by adopting a similar methodology. As a benchmark, he used the same credit scoring model with data from the Survey on Financing of Small and Medium Enterprises, 2000, but applied its methodology to firms in the CSBFP from the Survey on Financing of Small and Medium Enterprises, 2007. The author mentioned that the estimates from the former model were more reliable than the latter.

Recently, Seens and Song (2015) investigated the incrementality of the CSBFP. They applied the credit scoring model on data from the Statistics Canada Survey on Financing of Small and Medium Enterprises, 2011. Their sample contained 2,404 SMEs, 516 of which were CSBFP borrowers and 1,888 of which were non-CSBFP borrowers but had asked for debt financing in 2011. The authors found that the CSBFP was incremental and obtained a measure of incrementality of 67  percent.Footnote 8

## 4. Data Source

This paper used the Survey on Financing and Growth of Small and Medium Enterprises, 2014 linked to other data sources such as the General Index of Financial Information (GIFI) and Payroll Account Deductions (PD7) from 2010 to 2014.

The Survey on Financing and Growth of Small and Medium Enterprises, 2014 is a survey that collects information on the types of financing that businesses used in 2014, such as debt, lease, trade credit and equity financing, and government grants or subsidies. It considers to cover the demand side of financing for SMEs. It also contains other general business information such as growth, exports and innovation. Moreover, the survey provides information on the owner's characteristics, e.g., level of education, number of years of experience and gender.

The Survey on Financing and Growth of Small and Medium Enterprises, 2014 excludes any business with 0 employees, with 500 employees or more that has gross revenues of less than \$30,000, that is a non-profit organizationFootnote 9 or a public agency, and that is a member of specific business groups.Footnote 10

Conducted between February and May 2015, the survey, to which 10,397 businesses responded, had a response rate of 61 percent. These respondents form a representative sample of 621,417 SMEs of the survey's target population. For the CSBFP, the sample contains 743 firms representative of 1,833 businesses of the survey's target population.

In addition, the survey is augmented with administrative financial information from the GIFI such as total assets, total liabilities, total sales of goods and services, net income or loss after taxes and the annual average number of employees. Microdata from the Survey on Financing and Growth of Small and Medium Enterprises, 2014 and GIFI were only available at the Canadian Centre for Data Development and Economic Research (Statistics Canada) in 2016. This explains, among other factors, the lag between the year of the survey (2014) and the publication of this report.

For the purpose of this project, we restrained the sample to firms that had requested debt financing in 2014, more specifically, a non-residential mortgage or term loan. Indeed, under the CSBFP, firms can borrow for land or buildings used for commercial purposes, equipment or leasehold improvements. This is a first distinction from the work of Seens and Song (2015), since the authors included all types of debt financing (mortgages, lines of credit, term loans and credit cards). The reason was that the Survey on Financing and Growth of Small and Medium Enterprises, 2011 did not separate information by type of finnancing as it is done in the Survey on Financing and Growth of Small and Medium Enterprises, 2014.

Also, only incorporated firms are considered in this study, as the majority of firms in the CSBFP are incorporated. After eliminating outliers and missing observations, 1,748 SMEs were included in the sample.

Table 3 shows that the sample drawn from the Survey on Financing and Growth of Small and Medium Enterprises, 2014 contains approximately 8 percent of non-CSBFP borrowers whose debt financing request was rejected in 2014.

Table 3: Sample Size
- CSBFP
Participants
Non-CSBFP Participants
Rejected Borrowers
Non-CSBFP Participants
Approved Borrowers
Total
Total 710 110 1,223 2,043
Incomplete records 232 6 57 295
Total usable records 478 104 1,166 1,748

Sources: Statistics Canada, Survey on Financing and Growth of Small and Medium Enterprises, 2014; and Canada Revenue Agency, General Index of Financial Information 2013–2014, Payroll Account Deduction (PD7).

## 5. Methodology

In order to evaluate the incrementality of the CSBFP, we used the two-step approach mentioned in Section 3. In particular, we followed the same frameworkFootnote 11 employed by Seens and Song (2015). We first estimated the probability that a firm's application for a non-guaranteed loan would be approved, conditionally on explanatory variables related to the firm's performance metrics, characteristics and industry sector. The base equation that was estimated is given by

${y}_{i}^{*}$= β ${x}_{i}$ + ${\epsilon }_{i}$

where ${i}_{}^{}$ = 1, ..., N and ${y}_{i}^{*}$ denote a latent dependent variable that represents the propensity of a firm's loan application to be approved or denied. In the equation, ${\epsilon }_{i}$ denotes the error term. As the latent variable is not directly observed in the data, we will use another variable, denoted by ${y}_{i}^{}$ , which is 1 if the application was approved and 0, otherwise. This binary variable indicates in which category ${y}_{i}^{*}$  falls:

${y}_{i}=\left\{\begin{array}{c}1\mathrm{if}{y}_{i}^{*}>0;\\ 0\mathrm{if}{y}_{i}^{*}\le 0.\end{array}$

A logit model was used to estimate the probability that the firm's loan application would be approved:

$P\left({y}_{i}={1|x}_{i}\right)=\frac{\mathrm{exp}\left(\beta {x}_{i}\right)}{1+\mathrm{exp}\left(\beta {x}_{i}\right)}$

The second stage of the analysis used the resulting model to classify CSBFP loans as to whether or not the applications would have been rejected in the absence of the CSBFP. At the extreme, if the guaranteed loans were incremental and the model is reliable, the model would predict that all of the CSBFP loans would have been turned down. The proportion of such loans that the model predicts as being rejected is, under this logic, a direct measure of incrementality.

## 6. Variables

For the dependent variable, we used a dummy variable of 1 if the debt financing request for a mortgage or term loan was approvedFootnote 12 and 0, otherwise. In this case, a request is said to be approved if the partial or full amount was authorized.

The variables included in the logit regression are presented in Table 4. They are related to the firm's characteristics, such as age and size, or the firm's performance metrics, represented by debt ratio or net income or loss. The model also contains control variables such as province or territory and industry sector.

Table 4: Definition of Variables
Variable Definition
Age (lnage) Number of years until 2014 since the firm was first established. The natural logarithm is used in the model.Footnote 13
Size (lnsize) Average number of employees in 2014 as reported to the Canada Revenue Agency (Payroll Deductions and Remittances, PD7). The natural logarithm is used in the model.
Debt ratio (lag_debt_assets) Total liabilities in 2013 divided by total assets in 2013.
Net income or loss (net_income) Net income or loss reported in 2014, in 100,000 dollars.
Province (Reference: Alberta) =1 if the firm is located in the province, group of provinces and/or territories and 0, otherwise;

Atlantic (at) [New Brunswick, Nova Scotia, Prince Edward Island, Newfoundland and Labrador], Quebec (qc), Ontario (ont), Manitoba (man), Saskatchewan (sas) and British Columbia and Territories (bct).

Industry sector (Reference: retail trade) =1 if the firm is in the industry sector or group of industry sectors and 0, otherwise;

Agriculture, forestry, fishing and hunting (agr), mining, quarrying and oil and gas extraction (mining), manufacturing (manuf), construction (construc), wholesale trade (whole), transportation and warehousing (transp), professional, scientific and technical services (prof), accommodation and food services (accom), other services (other) [except public administration], all other (all_other) [information and cultural industries, finance and insurance, real estate and rental and leasing, management of companies and enterprises, administrative and support services, waste management and remediation services, health care and social assistance, and arts, entertainment and recreation].

All the variables used in the model were from 2014, except for debt ratio (total liabilities over total assets). This variable is lagged one year in order to avoid endogeneity caused by the simultaneity effect. The dependent variable, which is related to the approval or rejection of the firm's debt financing request, could have a causal effect on total liabilities, and therefore, on the debt ratio. This could be explained by the fact that because the request was approved, the total amount of liabilities subsequently increased. Table 5 shows the mean values of the explanatory variables that appear in the logit model.

Table 5: Mean Values of Explanatory Variables by Type of Borrower—Approved, Rejected and CSBFP
Variable Approved Rejected CSBFP
Business Characteristics Age (years) 24.59 17.12 13.03
Size (number of employees) 41.27 14.91 13.33
Performance Metrics Debt ratio 0.73 1.13 0.83
Net income (\$) 274,558.01 61,583.55 11,928.97
Industry SectorFootnote 14 (%) Accommodation and food services 9.26 13.46 14.23
Construction 12.44 13.46 15.06
Manufacturing; agriculture, forestry, fishing and hunting; mining, quarrying, and oil and gas extraction 24.96 15.38 15.48
Wholesale trade; professional, scientific and technical services; other services 21.27 18.27 15.06
Transportation 10.98 10.58 14.44
- 9.86 15.38 12.13
Regional DistributionFootnote 15 (%) Quebec; Atlantic 38.59 31.73 47.07
Ontario 26.24 30.77 19.67
Manitoba; Saskatchewan; Alberta 25.90 24.04 24.06
British Columbia; Territories 9.26 13.46 9.21
Number of observations 1,166 104 478

Sources: Statistics Canada, Survey on Financing and Growth of Small and Medium Enterprises, 2014; Canada Revenue Agency, General Index of Financial Information 2013–2014, Payroll Account Deduction (PD7); and author's calculations.

It is interesting to see that there are some similarities between Figure 2 and Table 5. Indeed, businesses whose debt financing requests were approved are older and larger than those whose requests were rejected.

Table 6 shows the distribution of debt financing request outcome by industry sector. With the exception of the "All other" industry sectors category, accommodation and food services has the lowest approval rate for debt financing requests. This is also in line with Figure 2.

Table 6: Distribution of Industry Sector by Debt Financing Request Outcome (approved/rejected)
Industry Sector Approved Rejected
Accommodation and food services 88.52 11.48
Construction 91.19 8.81
Manufacturing; agriculture, forestry, fishing and hunting; mining, quarrying, and oil and gas extraction 94.79 5.21
Wholesale trade; professional, scientific and technical services; other services 92.88 7.12
Transportation 92.09 7.91
All otherFootnote * 87.79 12.21
Number of observations 1,166 103

Sources: Statistics Canada, Survey on Financing and Growth of Small and Medium Enterprises, 2014; and Canada Revenue Agency, General Index of Financial Information 2013–2014, Payroll Account Deduction (PD7).

Overall, firms that were approved were in a better position in terms of performance metrics, in comparison with firms that were rejected. Indeed, the debt ratio in 2013 was lower for businesses with approved loans than for those with denied loans. This shows that the former were in a better position to borrow, with less debt per asset. Also, net income was greater for businesses whose loan requests are approved than for businesses whose loan requests are rejected. Higher net income suggests to lenders that borrowers are in good position to pay back their loans.

## 7. Results

In this section, we will show the estimated results (Table 7) obtained by the logit model.Footnote 16 This is the first step of our methodology, i.e., estimating a credit score model on businesses that are not CSBFP participants.

Table 8 presents the average marginal effects associated with the model. These are useful in assessing the extent of the impact of a variation in the corresponding explanatory variable on the firm's probability of being approved or rejected. We know that the estimated coefficients (as in Table 7) do not say anything about the extent of the impact of the explanatory variable on the dependent variable, but only the direction of the effect (Wooldridge, 2009). For that reason, we will consider only the average marginal effects in the analysis of the results of the credit scoring model.

Table 7: Estimation Results of the Logit Model (approval/rejection of the loan requests)
Variable Estimated Coefficient p–value
lnsize 0.447*** 0.000
Performance Metrics lag_debt_assets − 0.515*** 0.001
net_income 0.050** 0.034
Industry Sector Dummy
agr +** X
mining X
construc 0.251 0.580
manuf + X
whole + X
transp 0.511 0.270
prof 0.760 0.205
accom − 0.169 0.706
other_services + X
all_other 0.168 0.703
Regional Dummy
(Reference: Alberta)
bct 0.209 0.640
man +* X
ont 0.097 0.783
qc 0.589 0.109
at 0.560 0.211
Constant c 0.378 0.508
Number of observations 1,270 - -
Pseudo R2 0.129 - -
Log likelihood -313.302 - -

Note 1: Coefficients significant at * 10%; ** 5%; *** 1%.
Note 2: Standard errors were estimated using the bootstrap method (5,000 replications).
Note 3: "X" indicates that data were suppressed to meet confidentiality requirements of the Statistics Act. Some estimated coefficients are missing for this reason; however, their significance and sign appear.

Sources: Statistics Canada, Survey on Financing and Growth of Small and Medium Enterprises, 2014; Canada Revenue Agency, General Index of Financial Information 2013–2014, Payroll Account Deduction (PD7); and author's calculations.

All of the variables related to business characteristics are positive and significant, which means that older and larger businesses have a greater chance of having their debt financing request approved. Also, average marginal effects show that firm size has a greater impact on the firm's probability of being approved. Regarding performance metrics, Table 8 reveals that debt ratio (i.e., total liabilities divided by total assets) has a negative impact.Footnote 17 Thus, a higher debt ratio decreases the firm's probability of having a debt financing request approved. This shows that firms with a larger amount of liabilities than assets in 2013 had a lower chance of getting approved for their loans in 2014. Indeed, firms with a large amount of debt but fewer assets may signal to lenders that they have fully reached their borrowing capacity.

Table 8: Average Marginal Effects
Variable Estimated Average
Marginal Effects
p–value
lnsize 0.031*** 0.000
Performance Metrics lag_debt_assets − 0.035*** 0.001
net_income 0.003** 0.043
Industry Sector Dummy
agr +*** X
mining X
construc 0.016 0.552
manuf + X
whole +* X
transp 0.030 0.208
prof 0.042 0.103
accom − 0.012 0.713
other_services + X
all_other 0.011 0.689
Regional Dummy
(Reference: Alberta)
bct 0.013 0.607
man +*** X
ont 0.007 0.772
qc 0.037 0.070
at 0.033 0.134
Number of observations 1,270 - -

Note 1: Coefficients significant at * 10%; ** 5%; *** 1%.
Note 2: Standard errors were estimated using the bootstrap method (5,000 replications).
Note 3:"X" indicates that data were suppressed to meet confidentiality requirements of the Statistics Act. Some estimated coefficients are missing for this reason; however, their significance and sign appear.

Sources: Statistics Canada, Survey on Financing and Growth of Small and Medium Enterprises, 2014; Canada Revenue Agency, General Index of Financial Information 2013–2014, Payroll Account Deduction (PD7); and author's calculations.

Net income has a positive impact on the firm's probability of being approved for loans. Firms with a higher net income are in a better position to borrow, as lenders would see them as being able to repay the loans. However, the impact of this variable is smaller (i.e., the average marginal effect is smaller in absolute value) compared with firm age, firm size and debt ratio. Therefore, net income has less impact than the previous variables on the firm's probability of having its debt financing request approved.

The next step is to apply the estimated coefficients obtained in the previous model to the business variables of CSBFP borrowers. The calculations lead to an estimation of the binary variable on the status of the debt financing request (approved/rejected) or, in other words, of the credit scoring result obtained. Since the variable estimated is continuous, we transformed it into a dummy variable (0 or 1) by classifying firms into two categories. A CSBFP borrower is "approved" (=1) if the estimated dependent variable is greater than 0.925, and "rejected" (=0) otherwise.Footnote 18 It should be noted that Seens and Song (2015) also used this method to classify businesses in the CSBFP. The results are presented in Table 9.

Table 9: Approved/Rejected CSBFP Borrowers Based on the Logit Model, 2014
Total Number of Borrowers Approved Rejected
478 147
(30.75%)
331
(69.25%)

Sources: Statistics Canada, Survey on Financing and Growth of Small and Medium Enterprises, 2014; Canada Revenue Agency, General Index of Financial Information 2013–2014, Payroll Account Deduction (PD7); and author's calculations.

Approximately 69 percent of CSBFP borrowers would not have been approved for debt financing by a financial institution in 2014 if the CSBFP did not exist. Therefore, the CSBFP is incremental and the measure of incrementality of the program is 69 percent.

## 8. Conclusion

The aim of this paper was to measure the level of incrementality of the Canada Small Business Financing Program (CSBFP). The CSBFP is a statutory loan loss-sharing program governed by the Canada Small Business Financing Act (CSBFA) and administered by Innovation, Science and Economic Development Canada (ISED). Under the CSBFP, the Government of Canada and the private lenders share the burden of risk on loans in order to facilitate access to financing for small and medium enterprises (SMEs). Data suggest that SMEs that are young, smaller in size and in some specific industry sectors such as accommodation and food services, may suffer from credit rationing.

A program is said to be incremental if a sufficient proportion of SMEs would have had their financing request denied if the program did not exist. In this study, the incrementality of the CSBFP was measured based on the following methodology, consisting of a two-step approach:

1. Estimating a model similar to a bank's credit scoring model, applied to non-CSBFP borrowers. The model used is a logit model where the dependent variable is a binary variable that relates to the approval or refusal of a business's debt financing request; and
2. Using the previous estimated model to predict which CSBFP borrowers would have had their debt financing request approved or denied. The estimated proportion of businesses that would have been denied is the level of incrementality for the CSBFP.

Using the Survey on Financing and Growth of Small and Medium Enterprises, 2014 linked to the Canada Revenue Agency General Index of Financial Information 2010–2014 and Payroll Account Deduction (PD7), we obtain a level of incrementality of 69 percent. Thus, the proportion of CSBFP borrowers whose debt financing requests would have been rejected is 69 percent if the program did not exist. Finally, if partial incrementality was included as part of this analysis, it is expected that almost all CSBFP loans would be considered incremental.

## References

• Banerjee, R. (2014). "SMEs, Financial Constraints and Growth." BIS Working paper No. 475, Bank for International Settlements.
• Coe, C. (2016). "SME Profile: Canada Small Business Financing Program Borrowers." Ottawa: Innovation, Science and Economic Development Canada.
• Equinox (2003). "Incrementality of CSBF Program Lending, Volume 1: Insights from SME FDI Data." Equinox Management Consultants Ltd.